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PP DocBlockLayout Onnx

A layout block localization model trained on a self-built dataset containing Chinese and English papers, PPT, multi-layout magazines, contracts, books, exams, ancient books and research reports using RT-DETR-L. The...

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Model source

Kind
Model
File size
123 MB
Version
vRevision bcbfaad
Updated
Oct 30, 2026
Downloads
317
Source
Hugging Face

Source description

PP-DocBlockLayout

Introduction

A layout block localization model trained on a self-built dataset containing Chinese and English papers, PPT, multi-layout magazines, contracts, books, exams, ancient books and research reports using RT-DETR-L. The layout detection model includes 1 category: Region.

ModelmAP(0.5) (%)
PP-DocBlockLayout95.9

Note: the evaluation set of the above precision indicators is the self built version sub area detection data set, including Chinese and English papers, magazines, newspapers, research reports PPT、 1000 document type pictures such as test papers and textbooks.

Model Usage

Install Dependencies

pip install -U paddleocr
pip install -U onnxruntime-gpu

CLI Usage

paddleocr layout_detection -i ./demo.jpg --model_name PP-DocBlockLayout --engine onnxruntime

Python API Usage

from paddleocr import LayoutDetection

model = LayoutDetection(
    model_name="PP-DocBlockLayout",
    engine="onnxruntime",
)
output = model.predict("./demo.jpg", batch_size=1)
for res in output:
    res.print()
    res.save_to_img(save_path="./output/")
    res.save_to_json(save_path="./output/res.json")

Sources

1 source
Hugging FacePrimaryVersion bcbfaadf1a9b7197def8c01337cd5bbbef261fbd
huggingface.co/PaddlePaddle/PP-DocBlockLayout_onnx/tree/bcbfaadf1a9b7197def8c01337cd5bbbef261fbd

Verified Oct 30

Model artifacts

1 artifact

inference.onnx

onnx · 123 MB · SHA-256 faeca9767c96…8288 · Hugging Face

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Source excerpts

3 excerpts

--- license: apache-2.0 library_name: PaddleOCR language: - en - zh pipeline_tag: image-to-text tags: - OCR - PaddlePaddle - PaddleOCR - layout_detection --- # PP-DocBlockLayout ## Introduction A layout block localization model trained on a self-built dataset containing Chinese and English papers, PPT, multi-layout magazines, contracts, books, exams, ancient books and research reports using RT-DETR-L. The layout detection model includes 1 category: Region. | Model| mAP(0.5) (%) | | --- | --- | |PP-DocBlockLayout | 95.9 | **Note**: the evaluation set of the above precision indicators is the self built version sub area detection data set, including Chinese and English papers, magazines, newspapers, research reports PPT、 1000 document type pictures such as test papers and textbooks. ## Model Usage ### Install Dependencies ```shell pip install -U paddleocr pip install -U onnxruntime-gpu ``` ### CLI Usage ```shell paddleocr layout_detection -i ./demo.jpg --model_name PP-DocBlockLayout --engine onnxruntime ``` ### Python API Usage ```python from paddleocr import LayoutDetection model = LayoutDetection( model_name="PP-DocBlockLayout", engine="onnxruntime", ) output = model.predict("./demo.jpg", batch_size=1) for res in output: res.print() res.save_to_img(save_path="./output/") res.save_to_json(save_path="./output/res.json") ```

Source context: 317 downloads · 0 likes · Pipeline image-to-text · Library PaddleOCR · Repo PaddlePaddle/PP-DocBlockLayout_onnx

Source context: 53 downloads · 0 likes · Pipeline image-to-text · Library PaddleOCR · Repo PaddlePaddle/PP-DocBlockLayout_onnx